692 research outputs found
A Comparative Experiment of Several Shape Methods in Recognizing Plants
Shape is an important aspects in recognizing plants. Several approaches have
been introduced to identify objects, including plants. Combination of geometric
features such as aspect ratio, compactness, and dispersion, or moments such as
moment invariants were usually used toidentify plants. In this research, a
comparative experiment of 4 methods to identify plants using shape features was
accomplished. Two approaches have never been used in plants identification yet,
Zernike moments and Polar Fourier Transform (PFT), were incorporated. The
experimental comparison was done on 52 kinds of plants with various shapes. The
result, PFT gave best performance with 64% in accuracy and outperformed the
other methods.Comment: 8 pages; International Journal of Computer Science & Information
Technology (IJCSIT), Vol 3, No 3, June 201
Red Seal Landscape Horticulturist Identify Plants and Plant Requirements (F2 – 1&2): Line F Apply Horticultural Practices: F2 – Level 1 and 2
Red Seal Landscape Horticulturist Identify Plant Requirements is an adaptation of KPU HORT 1155 Introduction to Plant Materials Lecture Notes. This first edition supports student achievement of the Level 1 and 2 learning goals for Red Seal Landscape Horticulturist Line F2.This PDF is a representation of the book as it was on Feb.13, 2020. The online version may have been updated. For the most recent version, please visit the book url
Classification of Plants Using Images of their Leaves
Plant recognition is a matter of interest for scientists as well as laymen. Computer aided technologies can make the process of plant recognition much easier; botanists use morphological features of plants to recognize them. These features can also be used as a basis for an automated classification tool. For example, images of leaves of different plants can be studied to determine effective algorithms that could be used in classifying different plants. In this thesis, those salient features of plant leaves are studied that may be used as a basis for plant classification and recognition. These features are independent of leaf maturity and image translation, rotation and scaling and are studied to develop an approach that produces the best classification algorithm. First, the developed algorithms are used to classify a training set of images; then, a testing set of images is used for verifying the classification algorithms
Advanced shape context for plant species identification using leaf image retrieval
International audienceThis paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach
Feature extraction and automatic recognition of plant leaf using artificial neural network
Plant recognition is an important and challenging task. Leaf recognition plays an important role in plant recognition and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. From the view of plant leaf morphology (such as shape, dent, margin, vein and so on), domain-related visual features of plant leaf are analyzed and extracted first. On such a basis, an approach for recognizing plant leaf using artificial neural network is brought forward. The prototype system has been implemented. Experiment results prove the effectiveness and superiority of this method
Deciduous Trees for South Dakota Landscapes
This publication was prepared to meet the need for an informative reference on deciduous trees in South Dakota. Also, this publication will be of regional value due to its rather extensive treatment of cultivars, including hybrid and clonal varieties
Deciduous Trees for South Dakota Landscapes
This publication was prepared to meet the need for an informative reference on deciduous trees in South Dakota. Also, this publication will be of regional value due to its rather extensive treatment of cultivars, including hybrid and clonal varieties
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery
Fine-grained object recognition that aims to identify the type of an object
among a large number of subcategories is an emerging application with the
increasing resolution that exposes new details in image data. Traditional fully
supervised algorithms fail to handle this problem where there is low
between-class variance and high within-class variance for the classes of
interest with small sample sizes. We study an even more extreme scenario named
zero-shot learning (ZSL) in which no training example exists for some of the
classes. ZSL aims to build a recognition model for new unseen categories by
relating them to seen classes that were previously learned. We establish this
relation by learning a compatibility function between image features extracted
via a convolutional neural network and auxiliary information that describes the
semantics of the classes of interest by using training samples from the seen
classes. Then, we show how knowledge transfer can be performed for the unseen
classes by maximizing this function during inference. We introduce a new data
set that contains 40 different types of street trees in 1-ft spatial resolution
aerial data, and evaluate the performance of this model with manually annotated
attributes, a natural language model, and a scientific taxonomy as auxiliary
information. The experiments show that the proposed model achieves 14.3%
recognition accuracy for the classes with no training examples, which is
significantly better than a random guess accuracy of 6.3% for 16 test classes,
and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition
and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on
Geoscience and Remote Sensing (TGRS), in press, 201
Automatic Plant Detection Using HOG and LBP Features With SVM
Plants play a vital role in the cycle of nature. Plants are the only organisms which produce food by converting light energy from the sun. They also help in maintaining oxygen balance on earth by emitting oxygen and taking carbon dioxide. They have plenty of use in medicine and industry. But plant species are vast in number. To identify this large number of existing plant species in the world is a tedious and time-consuming task for a human. Hence, an automatic plant identification tool is very useful even for experienced botanists to identify the vast number of plants. In this paper, we proposed a technique to identify the plant leaf images. For training and testing, we used a publicly available dataset called Flavia leaf dataset. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are used to extract features and multiclass Support Vector Machine (SVM) is applied to classify the leaf images. We observed that the accuracy of HOG+SVM with HOG feature extraction using cells size of 2 x 2, 4 x 4 and 8 x 8 are 77.5%, 81.25% and 85.31 respectively. The accuracy of LBP+ SVM is 40.6% and the combination of HOG and LBP based features with SVM achieved 91.25% accuracy. The experimental results indicate the effectiveness of HOG+LBP with SVM over HOG+SVM and LBP+SVM techniques.
- …